# Langgraph

> Build multi-agent AI systems with LangGraph — the low-level orchestration

- **Type:** Skill
- **Install:** `agentstack add skill-magnus919-agent-skills-langgraph`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [magnus919](https://agentstack.voostack.com/s/magnus919)
- **Installs:** 0
- **Category:** [AI & ML](https://agentstack.voostack.com/c/ai-and-ml)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [magnus919](https://github.com/magnus919)
- **Source:** https://github.com/magnus919/agent-skills/tree/main/langgraph

## Install

```sh
agentstack add skill-magnus919-agent-skills-langgraph
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# LangGraph

LangGraph is LangChain's low-level orchestration framework for building stateful, long-running, multi-agent AI workflows using directed graph architectures (inspired by Pregel/Beam and NetworkX). It models agents as **nodes** in a graph, with **edges** controlling flow — enabling cycles, conditional branching, parallel execution, human-in-the-loop, and subgraph composition that linear chains cannot express.

This skill covers all major patterns for building and deploying LangGraph systems: core graph architecture, the three canonical multi-agent patterns (supervisor, swarm, hierarchical), persistence and state management, production debugging, and evaluation methodology.

> **Before you begin:** Install dependencies:
> ```bash
> pip install langgraph langchain langchain-openai langsmith
> ```

## Quick Start

Create your first LangGraph agent in under 10 lines:

```python
from langgraph.graph import StateGraph, MessagesState, START, END

def hello_agent(state: MessagesState):
    return {"messages": [{"role": "ai", "content": "Hello, world!"}]}

graph = StateGraph(MessagesState)
graph.add_node("agent", hello_agent)
graph.add_edge(START, "agent")
graph.add_edge("agent", END)
graph = graph.compile()

graph.invoke({"messages": [{"role": "user", "content": "hi!"}]})
```

**Next steps:**
1. Use the **Pattern Selection Guide** below to choose supervisor, swarm, or hierarchical architecture — each pattern links to its recommended template
2. Load the corresponding reference file for the deep pattern walkthrough
3. Use the **Choosing Your Starting Point** table below to pick scaffold, template, or reference based on your task
4. For a complete runnable example matching your pattern, use the linked template in assets/templates/

> **Design Principles — These Govern Every Graph Decision**
> 1. **State is the source of truth** — all inter-node communication happens through state, not through side channels or global variables.
> 2. **Nodes are pure-ish** — a node receives state, does work, returns updates. It should not depend on state that isn't passed to it.
> 3. **Reducers prevent conflicts** — any state key written by multiple nodes in parallel MUST have a reducer.
> 4. **Start simple** — a single agent with good prompts beats a multi-agent system with bad routing. Add agents only when a single prompt or toolset becomes unwieldy.
> 5. **Use `Send()` for dynamic fan-out** — when you don't know how many workers you'll need at compile time, spawn them dynamically from the orchestrator node.
> 6. **Subgraph state isolation** — subgraphs with different state schemas need a wrapper function to transform state at the boundary. Shared-schema subgraphs can be added directly as nodes.

## When to Reach For This

| Context | What to load |
|---------|-------------|
| Building a new LangGraph workflow from scratch | `references/architecture.md` — core concepts first |
| Designing a multi-agent routing system | `references/multi-agent-supervisor.md` or `references/multi-agent-swarm.md` — compare patterns |
| Composing nested agent teams | `references/multi-agent-hierarchical.md` — subgraph composition |
| Adding persistence, interrupts, or long-term memory | `references/persistence.md` — checkpointers and stores |
| Deploying to production or debugging failures | `references/production.md` — deployment, observability, failure modes |
| Setting up eval pipelines for routing accuracy | `references/evals.md` — evaluation methodology |
| Diagnosing a specific failure (loop, context loss, crash) | `references/troubleshooting.md` — known failure modes |

## Pattern Selection Guide

| Your constraint | Prefer | Why |
|----------------|--------|-----|
| Routing accuracy > latency | **Supervisor** | Centralized routing node, focused prompt: ~94% accuracy | `assets/templates/supervisor-graph.py` |
| Latency is primary constraint | **Swarm** | Direct agent-to-agent handoffs, ~40% fewer LLM calls | `assets/templates/swarm-graph.py` |
| Clear domain boundaries | **Swarm** | Agents rarely misroute, handoffs are crisp | `assets/templates/swarm-graph.py` |
| Ambiguous domain boundaries | **Supervisor** | Overlapping concerns resolved by dedicated router | `assets/templates/supervisor-graph.py` |
| < 3 distinct domains | **Skip multi-agent** | A specialized single agent is simpler | `references/architecture.md` |
| Multi-domain requests common | **Swarm** | Latency savings compound across handoffs | `assets/templates/swarm-graph.py` |
| Need centralized audit trail | **Supervisor** | Every routing decision visible in traces | `assets/templates/supervisor-graph.py` |
| Nested team structures | **Hierarchical** | Subgraphs as nodes, each team self-contained | `assets/templates/subgraph-agent.py` |

## Choosing Your Starting Point

| Your goal | Start with | Why |
|----------|------------|-----|
| Build a project from scratch, need generated code | `scripts/lg-supervisor-scaffold.py` or `scripts/lg-swarm-scaffold.py` | Scaffolds generate complete project structure (state.py, agents.py, graph.py) with placeholders to fill in |
| Understand a complete, working example | `assets/templates/` matching your chosen pattern | Templates are self-contained runnable files with all patterns wired — best for learning by reading |
| Deep dive into a pattern's internals | Corresponding reference in `references/` | References explain tradeoffs, failure modes, and design rationale — best for customization |
| Debug or optimize an existing system | `references/production.md` or `references/troubleshooting.md` | Production reference covers deployment + observability; troubleshooting reference covers symptom→fix tables |

## Core Primitives

LangGraph uses two APIs:

| API | When to use | Pattern |
|-----|-------------|---------|
| **Graph API** (`StateGraph`) | Full control over graph structure, conditional edges, subgraphs | `add_node()` + `add_edge()`/`add_conditional_edges()` |
| **Functional API** (`@task` + `@entrypoint`) | Simpler linear workflows, less boilerplate | Decorator-based, Pythonic |

Both APIs produce the same compiled graph — choose based on how much control you need.

## Key Gotchas

- **Subgraph persistence defaults to per-invocation** — each subgraph call starts fresh. Set `checkpointer=True` for per-thread memory, `checkpointer=False` for fully stateless.
- **Per-thread subgraphs cannot run in parallel** — same-namespace checkpoint conflicts. Use `ToolCallLimitMiddleware` or disable parallel tool calls.
- **The supervisor bottleneck** — every interaction requires a routing LLM call, even for obvious intents. Add a fast-path classifier (keyword matching or small model) for unambiguous requests.
- **Swarm ping-pong** — no natural recursion guard. Track `handoff_count` in state and hard-limit at 3, then escalate to human or fallback agent.
- **Lost messages on handoff** — `Command.update` must include paired messages from the specialist's tool-calling loop, or the next agent sees malformed history.
- **State access from parent to subgraph** — subgraphs manage their own checkpoint namespace. Use Store for cross-graph-boundary data.
- **Checkpoint bloat** — long conversations accumulate checkpoints. Prune periodically or set retention policies on DB-backed checkpointers.
- **No auto-load-on-install** — skills aren't auto-discovered at session start by name mention. The agent must explicitly call `skill_view(name='langgraph')` to load this skill.

## Reference Files

| File | Load when |
|------|-----------|
| `references/architecture.md` | You need to understand LangGraph core concepts: graph structure, nodes, edges, state, the two APIs, and basic agent loop construction. Read this first if you're new to LangGraph. |
| `references/multi-agent-supervisor.md` | You're designing a supervisor-based multi-agent system with a central routing node. Contains architecture, structured output routing, specialist wrappers, and full code examples. |
| `references/multi-agent-swarm.md` | You're designing a swarm-based multi-agent system with direct agent-to-agent handoffs. Contains handoff tool patterns, Command-based routing, and comparative metrics vs supervisor. |
| `references/multi-agent-hierarchical.md` | You're composing nested agent teams using subgraphs. Covers subgraph wiring (shared vs different state schemas), persistence modes, namespace isolation, and hierarchical team structures. |
| `references/persistence.md` | You're adding checkpointer-based short-term memory or store-based long-term memory. Covers per-invocation vs per-thread vs stateless modes, checkpoint backends, and cross-thread memory patterns. |
| `references/production.md` | You're deploying a LangGraph system to production. Covers Agent Server deployment, LangSmith observability, streaming patterns, and common production failure modes with fixes. |
| `references/evals.md` | You're setting up evaluation pipelines for multi-agent systems. Covers routing accuracy, resolution coverage, LangSmith eval datasets, and LLM-as-judge evaluators. |
| `references/troubleshooting.md` | You're debugging a specific LangGraph failure. Covers routing loops, context loss, checkpointer conflicts, token waste, and state inspection techniques. |
| `assets/templates/supervisor-graph.py` | Runnable supervisor example with billing, tech support, and account specialists — fast-path classifier, structured output routing, audit trail, and recursion guard. |
| `assets/templates/swarm-graph.py` | Runnable swarm example with triage agent plus 3 specialists — direct agent-to-agent handoffs via Command, recursion guard, and full traceability. |
| `assets/templates/subgraph-agent.py` | Runnable subgraph composition examples — all 3 wiring patterns (different state schemas, shared state keys, per-thread with namespace isolation). |

## Scripts

| Script | What it does |
|--------|-------------|
| `scripts/lg-supervisor-scaffold.py` | Generates a complete supervisor pattern project with state schema, routing agent, specialist nodes, and graph assembly |
| `scripts/lg-swarm-scaffold.py` | Generates a complete swarm pattern project with handoff tools, triage agent, specialist agents, and conditional routing |
| `scripts/lg-eval-generator.py` | Generates evaluation datasets and runs LangSmith evaluators for routing accuracy and resolution coverage |

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [magnus919](https://github.com/magnus919)
- **Source:** [magnus919/agent-skills](https://github.com/magnus919/agent-skills)
- **License:** MIT

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** yes

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-magnus919-agent-skills-langgraph
- Seller: https://agentstack.voostack.com/s/magnus919
- Browse the marketplace: https://agentstack.voostack.com/browse

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Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.